De-anonymization attack on geolocated data

Sebastien Gambs, Marc Olivier Killijian, Miguel Nunez Del Prado Cortez

Resultado de la investigación: Contribución a una conferencia

26 Citas (Scopus)

Resumen

With the advent of GPS-equipped devices, a massive amount of location data is being collected, raising the issue of the privacy risks incurred by the individuals whose movements are recorded. In this work, we focus on a specific inference attack called the de-anonymization attack, by which an adversary tries to infer the identity of a particular individual behind a set of mobility traces. More specifically, we propose an implementation of this attack based on a mobility model called Mobility Markov Chain (MMC). A MMC is built out from the mobility traces observed during the training phase and is used to perform the attack during the testing phase. We design two distance metrics quantifying the closeness between two MMCs and combine these distances to build de-anonymizers that can re-identify users in an anonymized geolocated dataset. Experiments conducted on real datasets demonstrate that the attack is both accurate and resilient to sanitization mechanisms such as downsampling.
Idioma originalInglés
Páginas789-797
Número de páginas9
DOI
EstadoPublicada - 1 dic. 2013
Publicado de forma externa
EventoProceedings - 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2013 -
Duración: 1 dic. 2013 → …

Conferencia

ConferenciaProceedings - 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2013
Período1/12/13 → …

Palabras clave

  • de-anonymization
  • geolocation
  • inference attack
  • Privacy

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